Instructions to use cturan/Olmo-3-7B-Instruct-Q1_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cturan/Olmo-3-7B-Instruct-Q1_0",
	filename="olmo3-7b-1bit.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cturan/Olmo-3-7B-Instruct-Q1_0
# Run inference directly in the terminal:
llama cli -hf cturan/Olmo-3-7B-Instruct-Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cturan/Olmo-3-7B-Instruct-Q1_0
# Run inference directly in the terminal:
llama cli -hf cturan/Olmo-3-7B-Instruct-Q1_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cturan/Olmo-3-7B-Instruct-Q1_0
# Run inference directly in the terminal:
./llama-cli -hf cturan/Olmo-3-7B-Instruct-Q1_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cturan/Olmo-3-7B-Instruct-Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cturan/Olmo-3-7B-Instruct-Q1_0

Use Docker

docker model run hf.co/cturan/Olmo-3-7B-Instruct-Q1_0

LM Studio
Jan
Ollama
How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Ollama:
```
ollama run hf.co/cturan/Olmo-3-7B-Instruct-Q1_0
```

Unsloth Studio

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cturan/Olmo-3-7B-Instruct-Q1_0 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cturan/Olmo-3-7B-Instruct-Q1_0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cturan/Olmo-3-7B-Instruct-Q1_0 to start chatting

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Olmo-3-7B-Instruct-Q1_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cturan/Olmo-3-7B-Instruct-Q1_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Olmo-3-7B-Instruct-Q1_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default cturan/Olmo-3-7B-Instruct-Q1_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Olmo-3-7B-Instruct-Q1_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "cturan/Olmo-3-7B-Instruct-Q1_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Docker Model Runner:
```
docker model run hf.co/cturan/Olmo-3-7B-Instruct-Q1_0
```

Lemonade

How to use cturan/Olmo-3-7B-Instruct-Q1_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cturan/Olmo-3-7B-Instruct-Q1_0

Run and chat with the model

lemonade run user.Olmo-3-7B-Instruct-Q1_0-{{QUANT_TAG}}

List all available models

lemonade list

OLMo-3 7B Instruct (1-Bit Experimental)

This is an experimental 1-bit quantized version of the OLMo-3 7B Instruct model. It was developed using Quantization Aware Distillation (QAD) techniques. Notably, the entire architecture, including the embeddings, has been fully compressed to 1-bit.

Current Development Status

The model was trained for 12 hours on a cluster of 4x B200 GPUs. Please note that it currently serves as a technical proof of concept and is not intended for production environments.

Performance: The model is capable of processing basic English and short sequences.
Known Issues: Due to the experimental nature and training duration, users may encounter frequent repetition loops and limited context tracking.

Usage and Implementation

The required 1-bit kernels have been merged into mainline llama.cpp, simply use any recent llama.cpp build.

llama-server -m olmo3-7b-1bit.gguf --port 8080

Future Roadmap

Future iterations will focus on extending the training duration and refining dataset selection. These steps are expected to significantly stabilize the 1-bit quantization and enhance the model's reasoning capabilities.

License: Apache 2.0 Base Model: allenai/Olmo-3-7B-Instruct